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It has been shown that modulating the saliency of a dense amount of information presented as icons on a map-based interface can reduce cognitive workload and improve user performance. Further, first response teams, particularly those responding to complex events, such as Chemical, Biological, Radiological, Nuclear and Explosive device incidents will incorporate robots into their future teams to assist human team members and collect additional information. The deployment of such robots will require a human team member to supervise and task the various robots associated with the team. As the complexity of an incident increases and the number of responders with different specialties increases, for example Police, Emergency Medical Services, and Hazardous Materials, it will be harder to track the robots associated with a particular team, especially by the human team member responsible for the robots. A new algorithm, the Robot Visualization Algorithm, was developed to improve the saliency of robots for which the human team operator (e.g., Emergency Medical Services) is responsible, while generally minimizing the saliency of the robots from other teams (e.g., Police and Hazardous Materials) that are not relevant to the team operator. The presented Robot Visualization Algorithm makes the other teams’ robots more salient if their activities will impact the operator’s team. The within-subjects evaluation determined that the Robot Visualization Algorithm allowed operators to have a better awareness and lower cognitive workload than a base visualization condition. A number of proposed algorithm refinements are also discussed.

Robotic rovers are expected to play a major role in future lunar insitu resource prospecting. Prospecting missions will involve a ground control team of planetary scientists and rover operators. These ground controllers will need to evaluate prospecting data gathered by a rover and make operational decisions in real-time. In October 2014, the NASA Ames Research Center conducted a lunar analog robotic prospecting mission in the Mojave Desert to study how to support such operations. This paper describes the roles within the Science Operations Team during this analog mission, as well as preliminary findings regarding the scientists' use of shared displays.

Complex human machine systems, including remotely deployed mobile robots and sensors can generate an overwhelming amount of data. Filtering the available geospatial information is necessary to make the most time critical information salient to the system operators. The General Visualization and Abstraction (GVA) algorithm abstracts the presented information in order to reduce visual clutter and has been shown to reduce the cognitive demands and perceived workload of a single operator tasked when supervising teams of multiple robots with high levels of autonomy [1,2]. My research focuses on significantly extending the GVA algorithm to support multiple human operators who share a common high level goal, but have role specific subgoals for their designated human-robot teams.